Current Talks

These are the talks for cSplash 2017.

Life is About Linearity I

Have you ever wondered about how your Netflix feeds you with new TV shows? And why do your friends have different feeds than you? Or, have you wondered why Facebook always post advertisements that you like?
Life is about linearity. You might be amazed at how linear algebra have to do with the answers.
In this 2-hour sequence, We (Vivian and Nikki), want to provide a vision into this fascinating world build up by matrix.

Prerequisites: 
Basic algebra
Professor: 
Vivian Cheng
Difficulty: 
Easy
Room: 
517
Size: 
40
Enrolled: 
11

Life is about linearity II

Do you ever wondered why Instagram has such powerful image processing functions? You can enlarge and crop, add filters, or compress any photos you take and make it fancy.
Life is about linearity. You might be amazed at how linear algebra have to do with the answers.
In this 2-hour sequence, We (Vivian and Nikki), want to provide a vision into this fascinating world build up by matrix.

Prerequisites: 
Basic Matrix computation. Or Life is about linearity I by Vivian Cheng
Professor: 
Yuan Ni
Difficulty: 
Medium
Room: 
517
Size: 
40
Enrolled: 
15

Math of Retiring Early!

Pretend you make money---what if you save some of it each year? Then, by age 65, you can retire and live the good life! But who wants to wait that long? What percent of my income should I save if I want to retire at age 55? 45? 35? 25? Younger? In this talk, we use simple assumptions to discover a formula relating percent of income saved to retirement age. At the end, we'll discuss some ways we can make our assumptions more realistic, to get a better formula. One way is to compound your interest more often--this leads to the number e and calculus.

Prerequisites: 
Basic algebra is necessary, since we're finding a formula for retirement age! As we'll be finding a sum of savings, information about geometric sums is helpful, though not required. For the last few minutes, knowledge of the number e and calculus help, but is not required if you're willing to accept a thing or two on faith.
Professor: 
Sam Ferguson
Difficulty: 
Medium
Room: 
317
Size: 
40
Enrolled: 
12

Mathematical Music Theory: A Non-Rigorous Introduction

Mathematical music theory uses modern mathematical structures to analyze works of music, characterize musical objects such as the consonant triad or the diatonic scale, and even to compose new music.

In this lecture, we will touch upon the deep connections between mathematics and music, explore the structure of both, and explain the relevance of their pursuits to human society. The interrelationships between algebra and harmony, combinatorics and rhythm, geometry and melody, and structure and form will be covered, as well as more specific areas of intersection.

Prerequisites: 
Basic algebra would be useful but is not necessary; all mathematical tools will be explained from the ground up. An enthusiasm for music and mathematics is important!
Professor: 
Vishnu Bachani
Difficulty: 
Easy
Room: 
1302
Size: 
60
Enrolled: 
17

Noise and Information in the Brain

Surprisingly little is known about even some of the most basic properties of neurons. In this talk, we'll discuss some of what is known (and also point out some of what isn't known) by trying to answer some of the following questions: 1) How do single neurons encode information? 2) How does noise corrupt and (surprisingly!) enhance the signal being sent from one neuron to another? 3) Where does the noise come from? 4) How do multiple neurons encode information together?

If time permits, we'll also discuss a simple neural network.

Prerequisites: 
Basic biology and probability would be helpful, but this talk will aim to be self-contained. Basic physics might also be helpful in the first few minutes of the talk.
Professor: 
William Redman
Difficulty: 
Medium
Room: 
312
Size: 
40
Enrolled: 
17

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